1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
6/2/2025 Comida 52730 Andrés supermercado (no cobre el otro de 25k pq muchas son cosas mías)
9/2/2025 Comida 12500 Andrés NA
17/2/2025 Comida 7940 Andrés NA
18/2/2025 Electricidad 64888 Andrés la puse por adelantado para que no se me olvide
18/2/2025 Comida 17820 Tami Supermercado
23/2/2025 Comida 86908 Tami Supermercado
27/2/2025 Comida 10000 Andrés NA
26/2/2025 Comida 4620 Andrés NA
1/3/2025 Comida 2300 Tami Supermercado
2/3/2025 Comida 102058 Tami Supermercado
3/3/2025 Comida 9370 Andrés NA
9/3/2025 Comida 61916 Tami Supermercado
11/3/2025 Comida 27021 Andrés NA
11/3/2025 Enceres 13190 Tami 40 rollos confort
15/3/2025 Comida 78061 Tami Supermercado
17/3/2025 Electricidad 52458 Andrés NA
17/3/2025 VTR 22000 Andrés NA
21/3/2025 Agua 19562 Andrés NA
22/3/2025 Comida 76766 Tami Supermercado
21/3/2025 Diosi 18500 Andrés antiparasitario
27/3/2025 Gas 82450 Andrés NA
26/3/2025 Comida 4000 Andrés avena multigrano y chucrut
29/3/2025 Comida 70591 Tami Supermercado
3/4/2025 Gas 83300 Andrés NA
4/4/2025 Agua 20807 Andrés NA
6/4/2025 Comida 52655 Tami Supermercado
12/4/2025 Comida 72108 Tami Supermercado
16/4/2025 VTR 21990 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 1.0045e+09   2    5.1314 0.0061 ** 
## lag_depvar    2.6291e+11   1 2686.0325 <2e-16 ***
## Residuals     8.1239e+10 830                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1850.888 16308.56 0.1482974
## 2-0 31382.375 23210.244 39554.51 0.0000000
## 2-1 24153.537 19421.498 28885.58 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
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## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
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## 25   24642.71             0   22529.57
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## 29   28706.00             0   28640.00
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## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 819  29568.00             2   36772.29
## 820  42362.00             2   29568.00
## 821  42566.29             2   42362.00
## 822  39596.00             2   42566.29
## 823  32925.00             2   39596.00
## 824  43416.57             2   32925.00
## 825  52624.86             2   43416.57
## 826  57733.71             2   52624.86
## 827  54120.57             2   57733.71
## 828  53353.43             2   54120.57
## 829  56286.86             2   53353.43
## 830  60626.86             2   56286.86
## 831  61375.29             2   60626.86
## 832  53710.86             2   61375.29
## 833  55795.57             2   53710.86
## 834  55130.14             2   55795.57
## 835  57700.14             2   55130.14
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   678 53616.64 22168.457
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29  29568.00  42362.00
## [820]  42566.29  39596.00  32925.00  43416.57  52624.86  57733.71  54120.57
## [827]  53353.43  56286.86  60626.86  61375.29  53710.86  55795.57  55130.14
## [834]  57700.14
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   2020.05516   4040.87288   -538.53416   2437.67080  -2970.54590    518.41377 
##            8            9           10           11           12           13 
##  -5656.33688  -1187.14601  -3965.40373   -416.55848  -4938.49517  -1607.34513 
##           14           15           16           17           18           19 
##   -897.68446    379.40369  -3241.33937   -375.91823  -2128.35372   6606.06236 
##           20           21           22           23           24           25 
##  -1529.22511  -1208.11190   1475.91541  -1186.80388    234.61603   1694.81652 
##           26           27           28           29           30           31 
##  -7102.68099    948.59248   8193.11250    417.06114    -15.05712  -2401.48857 
##           32           33           34           35           36           37 
##   1575.96012   4571.96293   1125.46528   2389.97263  -1869.76752   4606.84261 
##           38           39           40           41           42           43 
##   4303.11757  -2276.90145  -2981.94535  -1109.90649 -10740.98683   7292.15840 
##           44           45           46           47           48           49 
##   2558.04161   1366.94756   8104.94412    684.15104   6526.87232   6711.30301 
##           50           51           52           53           54           55 
##  -5886.51542  -4797.74343  -5060.63966  -7928.20356   6131.92839  -4076.15234 
##           56           57           58           59           60           61 
##  -4893.01505   3858.04044    888.88417    -31.42322    142.81606  -4995.96742 
##           62           63           64           65           66           67 
##  18128.30244   3637.89073  -3649.32316   5923.43578   7341.10452  14634.78250 
##           68           69           70           71           72           73 
##   1686.47819 -13218.81389  -1308.26885   4642.08632  -4902.48880  -4405.20142 
##           74           75           76           77           78           79 
## -10496.85168   2470.15533  -5397.24561   1067.49228  -6863.11120    551.56687 
##           80           81           82           83           84           85 
##  -2350.50336  -2689.56249  -3927.27601   -532.33182   2319.41557   3766.33281 
##           86           87           88           89           90           91 
##    479.09705   -482.79602    198.23009   4303.26811  -1163.00593   1151.14571 
##           92           93           94           95           96           97 
##  -2064.52315  -1043.79794    178.29356    275.35725  -7483.61029   2394.22466 
##           98           99          100          101          102          103 
##  -8600.90703  -2936.62641  -4034.93015  -1731.33689  -1255.84474   3186.36387 
##          104          105          106          107          108          109 
##  -2338.10017   2598.22681  -1155.40381    973.46576   2589.48700  -3153.00362 
##          110          111          112          113          114          115 
##  -4720.90655   -846.96631   1906.73253  11695.87578  -1243.84817   2667.81494 
##          116          117          118          119          120          121 
##   4261.52283   3500.40937  -1102.81242  -4718.42698  -3724.48755   2320.59590 
##          122          123          124          125          126          127 
##  -1732.47526   1341.17183   8858.62214    845.03650    128.50177  -2522.90852 
##          128          129          130          131          132          133 
##   2654.43543   7051.31659   1009.49901  -8502.17579   1749.22750   4135.10417 
##          134          135          136          137          138          139 
##  -3165.41618  -1420.06384   -853.80447  -3879.54736   1184.61882   -494.36584 
##          140          141          142          143          144          145 
##  -2912.42826   1720.07996  -1879.83801  -7827.63379   2043.19130  -3476.99563 
##          146          147          148          149          150          151 
##   2105.60588   -255.13692   1025.12439   -357.78334   1353.60355   1187.45896 
##          152          153          154          155          156          157 
##   3356.94465  -4862.43701  -1173.63990  -3234.73234   5958.60309   9746.59027 
##          158          159          160          161          162          163 
##  -3657.43879  -5003.11408   3380.36155    -30.45822   2470.42599  -6137.78638 
##          164          165          166          167          168          169 
##  -6973.02943   3932.99003  17166.11175   3386.57600   -642.28849  -2689.16177 
##          170          171          172          173          174          175 
##  -1345.41094   3350.00381   -471.31191  -8318.26136   2626.31836   4085.43271 
##          176          177          178          179          180          181 
##    382.02640   8506.38569  -9498.94264  -3714.91379 -10985.50902 -11473.89183 
##          182          183          184          185          186          187 
##   1007.75758   9063.14182  -1669.00335   5689.58831   6309.74399  12904.95017 
##          188          189          190          191          192          193 
##   8163.03369  -4340.79712   2189.66054  10089.68432  -1934.08023  -2733.12090 
##          194          195          196          197          198          199 
## -10566.16156  -6637.52059    966.28799  -5501.70422 -10058.38397   5131.99535 
##          200          201          202          203          204          205 
##  -3325.67667  -1966.45954  -1056.64183   6242.01928   9619.71569    301.26459 
##          206          207          208          209          210          211 
##   2646.30569   2815.91356   5499.00281  12542.16513  -5992.93206 -11591.37483 
##          212          213          214          215          216          217 
##  -5944.90867 -10857.26261  -5330.87764   1277.47559 -13262.16011  16153.52515 
##          218          219          220          221          222          223 
##   7550.31661   1257.65336  26415.36468  12216.26855   7010.00183  13695.59737 
##          224          225          226          227          228          229 
##  -4258.92571  -2074.65969   3451.86572     35.32125   2427.45969   8689.02897 
##          230          231          232          233          234          235 
##   5510.94857  -2223.98143  -2136.03455   9125.68993 -11815.84231  -7571.80984 
##          236          237          238          239          240          241 
##  -8817.92604 -10366.27492   2825.64650   1095.56730  -8555.57361  -9237.56760 
##          242          243          244          245          246          247 
##   8857.14213  -8017.06584   2244.70164 -10549.02057  -4289.79177   1189.39016 
##          248          249          250          251          252          253 
##    764.53533 -12558.72493   3414.61049   1824.64363   3967.56666   1881.46511 
##          254          255          256          257          258          259 
##  -1419.29845  10880.13503  20602.58925   2883.42849  -4588.94355   3800.59456 
##          260          261          262          263          264          265 
##  -2008.24817   3428.60203  -5164.09947 -11195.70088  -5011.61695   -796.93338 
##          266          267          268          269          270          271 
##  -5463.17860   8510.65942  -4564.91643   3911.04204  -2394.12013   4146.40965 
##          272          273          274          275          276          277 
##    415.52159   7007.61697  -1721.48116  11718.52620  -4914.53364   1404.66475 
##          278          279          280          281          282          283 
##   -695.34768   7530.55031  -5392.54930  -3052.78632 -11574.34509  -2955.80998 
##          284          285          286          287          288          289 
##  18374.53074   7462.61069   2397.98492   -967.85461    571.35469   6064.52499 
##          290          291          292          293          294          295 
##   6537.35320 -19128.89564 -11444.70345  -8396.64685   9410.98104   2794.52810 
##          296          297          298          299          300          301 
##  -1463.49139  27121.22458   9716.51817   4531.89829   9143.84672   2466.52890 
##          302          303          304          305          306          307 
##  -1419.98055   7521.26708 -24681.60631  -3845.61435   -471.09698  -7259.25340 
##          308          309          310          311          312          313 
##  -4240.17801   2676.88497  -9454.30989  -3464.95811  -8411.86634   1362.10525 
##          314          315          316          317          318          319 
##  -3361.82863   1843.31498  -4297.71267  27237.48243  -1035.45340   2983.23469 
##          320          321          322          323          324          325 
##  10514.39740   5244.37132  32025.33298   4671.34634 -21373.35078   1434.72246 
##          326          327          328          329          330          331 
##    757.16133  -6812.32492  -2053.52990 -33575.18318    715.66567  -2470.95892 
##          332          333          334          335          336          337 
##   -255.03485  -3330.92940   3930.34533   -608.83610  -7125.37162  -3269.37884 
##          338          339          340          341          342          343 
##  -2338.32564  -7823.63956   3727.49932  -1515.67157  -1883.62043  -1139.65364 
##          344          345          346          347          348          349 
##     28.44111    327.14866  -1780.23106  -9608.31228 -13346.12148   2210.04720 
##          350          351          352          353          354          355 
##  -4441.71886  -3771.32108  -6089.69635   1651.45305   1267.43695   2620.57862 
##          356          357          358          359          360          361 
##  -3918.89482   -664.07503    522.86836   6848.81971     79.34166   -240.72522 
##          362          363          364          365          366          367 
##   2377.20003  -2968.33037  -1086.07013  -8949.70477  -4799.39871  -6370.88477 
##          368          369          370          371          372          373 
##  -5088.36623  -7378.54711   4908.93604    234.56000   6973.20242  -7818.17342 
##          374          375          376          377          378          379 
##  -2418.96192  -3540.21882  -2611.48111 -12598.25559   1803.40524 -10751.67747 
##          380          381          382          383          384          385 
##   5610.07164   9214.52715   2956.65817  -2587.55839   1420.13536   6547.79032 
##          386          387          388          389          390          391 
##  11182.36876  -6080.38691  -5620.06011   -395.41845   8323.90488   1538.89266 
##          392          393          394          395          396          397 
##  10938.76161 -10206.72349   2488.09401    416.66173    265.96941   -949.71339 
##          398          399          400          401          402          403 
##   -853.38556 -14772.46683   8305.75247  -1429.55158  -1612.77584   6749.64271 
##          404          405          406          407          408          409 
##  -8192.80359  -1519.17653  -2745.46858  -6019.66552  -3032.75911  -4079.16091 
##          410          411          412          413          414          415 
##  -8902.66850   6019.47465   1491.71062  -7535.47804  -7827.28365  14112.29229 
##          416          417          418          419          420          421 
##   3632.65359   4283.56861  -8269.33870  -4942.71697  -2779.40258   2651.17660 
##          422          423          424          425          426          427 
## -14195.14289  -2916.88308  -9218.34494   2925.66682   6865.55027   6422.63709 
##          428          429          430          431          432          433 
##  -4176.75992  -4295.85799  -4882.67455  -1933.89124  -5854.15106  -6750.42505 
##          434          435          436          437          438          439 
##  -6052.76841  -1480.75128   -940.70961  -5075.56061   2491.81622   4726.53192 
##          440          441          442          443          444          445 
##  -5200.83336  -2287.67729   1448.22943  -3980.12542   2702.30596  -6731.05615 
##          446          447          448          449          450          451 
## -12241.04643  -4598.41010   9566.15943  -2162.65601   4625.43941  -6025.11291 
##          452          453          454          455          456          457 
##  -1258.14244    247.11621   2882.13293 -12430.33442   3253.37614  -6838.47936 
##          458          459          460          461          462          463 
##   6406.23269   2861.99032   2339.67281  -4026.58570   1925.70720   -185.87971 
##          464          465          466          467          468          469 
##   1612.45613   -710.85907   3161.86535  -2842.37646   5613.64994  -7156.81576 
##          470          471          472          473          474          475 
##  -3147.36688  -2375.31264  -4824.97032   2852.87325   7638.30272  -6209.88950 
##          476          477          478          479          480          481 
##   1316.63740  -6353.42005  -2994.58599   1870.61105 -13082.74669  -9860.78531 
##          482          483          484          485          486          487 
##  -1278.14590    -61.54745  -1054.26203  -1439.48901  -9685.97857  11021.94016 
##          488          489          490          491          492          493 
##   6108.12012   7262.87341  -5626.14180   5198.68275   9101.48885   5826.13554 
##          494          495          496          497          498          499 
## -13721.46644 -10755.27298  -3588.00496  -1241.69613   -659.39373  -7762.63749 
##          500          501          502          503          504          505 
##    500.50551   4169.15801   5368.48990    495.61230    -87.94885  -7408.58319 
##          506          507          508          509          510          511 
##    427.57809  -5195.69867   1701.94149  -1438.01518  -8297.63278   -714.47487 
##          512          513          514          515          516          517 
##  -2790.57730   -699.47594   1216.46643  -9621.84314  -7861.47628  24210.92452 
##          518          519          520          521          522          523 
##   9648.07736   5663.97178  -5570.90313   2587.72399  16800.08957  11192.53428 
##          524          525          526          527          528          529 
## -24465.38136  -5272.17634  -3923.20373   4397.58807   -549.34875 -11293.07948 
##          530          531          532          533          534          535 
##   4244.22257  13742.76615  -5198.29273   4173.92175   5339.24402  -2025.22706 
##          536          537          538          539          540          541 
##  -4764.64975  -7276.50899  -2273.31265   8157.94193    -69.96901  -8337.37297 
##          542          543          544          545          546          547 
##   1652.91523   -770.56212    197.16726 -11201.96404 -11191.28219   1948.60545 
##          548          549          550          551          552          553 
##   6895.73687  -1458.45064    697.56910  -7866.82488   8445.01572    748.89094 
##          554          555          556          557          558          559 
## -12107.72188   9041.87148   8495.99547    -98.77900   4655.25307  -3790.30468 
##          560          561          562          563          564          565 
##  13908.28514  21245.05248  -6797.68545  -9974.56943   6528.09155    -41.05323 
##          566          567          568          569          570          571 
##   3194.16322  -7643.64898 -17537.19999   6507.08623   6254.04316   1699.22067 
##          572          573          574          575          576          577 
##   2891.70799   1555.59354  -2381.42600  14513.51432  -9905.79915  -6458.25520 
##          578          579          580          581          582          583 
##   8524.67826   2640.09471  -6770.54621   7313.44358  -4021.28657  -2977.95019 
##          584          585          586          587          588          589 
##  15513.90449 -14745.50667   8242.42840   -145.38862  -6423.78452   -937.53041 
##          590          591          592          593          594          595 
##     73.74270 -10830.61138   1661.01557  -7288.98718   2948.32002   8731.74378 
##          596          597          598          599          600          601 
##  -7671.10665   5708.93489   2568.31409   6678.76583  -3391.80367   5962.99873 
##          602          603          604          605          606          607 
##  -8505.57268   2083.12500   1090.68099   2956.15384   1303.71130    203.59753 
##          608          609          610          611          612          613 
##  -6002.31145   7907.70461  -1378.99739  -2760.90175  -3627.02770  -8386.81279 
##          614          615          616          617          618          619 
##  11831.00422   4763.81311  -9502.70949  11475.14723   5857.49316  -5782.57536 
##          620          621          622          623          624          625 
##  26176.61424 -13095.22259  -6989.91080   2978.29698  -4345.14345 -10759.20081 
##          626          627          628          629          630          631 
##  11168.59966 -21803.46710  -2507.93230   8585.20721  11011.09167  -1716.37092 
##          632          633          634          635          636          637 
##  33127.63710  -6853.09503   5485.92443   5158.13735  -2517.14987  -5574.86310 
##          638          639          640          641          642          643 
##  -2142.58664 -12620.57179  -2386.08158  -2022.27063  -2650.60873  -2981.08930 
##          644          645          646          647          648          649 
##   1699.50661   4306.77113  16824.31567  18356.47666    630.56585   4543.50303 
##          650          651          652          653          654          655 
##  10360.48417  19876.07273    423.38712 -28364.95301  -1534.30332  -2471.02254 
##          656          657          658          659          660          661 
##   1703.55931  -3355.80353 -10769.50876   1552.89064   4109.84917  -1135.22331 
##          662          663          664          665          666          667 
##  12908.19951   1199.54421   1653.34484 -11851.78086   1255.92818   1061.45750 
##          668          669          670          671          672          673 
##  -5293.45924  -7521.03175   1976.93837  -3808.47613   2585.56558  -3476.14407 
##          674          675          676          677          678          679 
##  -9426.24523  -8374.75559  -3032.45095    116.90358   2783.13642    635.90795 
##          680          681          682          683          684          685 
##  -3910.13816  -1886.47561  -1396.36165  -8321.82078   4584.56443  -2323.85018 
##          686          687          688          689          690          691 
##  -1478.42947    506.41930  10767.18532   9734.42227  10486.28719  -9819.81049 
##          692          693          694          695          696          697 
##  -3680.38189  -3254.66524   5764.82342 -10504.28497  -8004.29661  -8687.35224 
##          698          699          700          701          702          703 
##  -6335.20212  -4792.38421   3032.09715  -4465.48173  -1958.04817   4160.47691 
##          704          705          706          707          708          709 
##  31031.20112   9404.78848  23329.17729   1558.35447   8212.11125  22814.89754 
##          710          711          712          713          714          715 
##   6453.84720 -18300.40130   4748.48883  -5514.16572   -164.04792    418.63558 
##          716          717          718          719          720          721 
## -17326.30255  -5313.56504   3288.17486  -3062.20788 -13025.56804   4239.25990 
##          722          723          724          725          726          727 
##  -5600.20131    700.64819  -3978.03897 -12489.44025   1331.01750  -1906.26047 
##          728          729          730          731          732          733 
##  -9817.28500  17232.95020   1727.83234  -2771.63793   5667.55543  -8681.67311 
##          734          735          736          737          738          739 
##   -769.70555   8091.86737 -15402.89074  -5953.95609   7367.32639  -4830.79871 
##          740          741          742          743          744          745 
##    116.45229   1782.30980  -2003.06187  -5214.73954   6368.13967  -6323.83539 
##          746          747          748          749          750          751 
##  22652.97428   7769.15180  -2007.60439  -7346.22778  23363.40768  -4354.11693 
##          752          753          754          755          756          757 
##   1338.24561 -14478.95636  56060.42262  26854.02857  15027.11712 -10711.04694 
##          758          759          760          761          762          763 
##  10552.57495   7261.56144   5758.66710 -46437.66224 -16201.60924    940.80723 
##          764          765          766          767          768          769 
##  -2543.87422  -3483.83084 122818.53473  19267.69308  43678.88775  22543.55576 
##          770          771          772          773          774          775 
##  12035.87570  15809.12364  25690.49317 -98845.87936  -6781.28505 -35854.93255 
##          776          777          778          779          780          781 
##   1717.71601  -1248.92517   3375.73478  -7435.71869  -1465.29194  -1965.60008 
##          782          783          784          785          786          787 
##   3404.14602  -7175.84083  -2256.63147   3899.76637   2245.40140  -2778.93466 
##          788          789          790          791          792          793 
##  -4066.65335   1720.30610   2834.88511    -69.89590  -6721.54279  -5799.80376 
##          794          795          796          797          798          799 
##  -1164.12878  -1279.96333  -7834.14149  -2370.30952  -3284.67044  -2682.07413 
##          800          801          802          803          804          805 
##  10707.74009   2223.50116   7061.85598   2905.18348  -5466.44414   8170.26285 
##          806          807          808          809          810          811 
##   9856.95100 -10620.38780  -7412.74379  -7520.23838   2991.84884   4179.83419 
##          812          813          814          815          816          817 
##  -2284.45379 -14179.38508  -4123.14184   6247.20967   8223.67196  -9687.66480 
##          818          819          820          821          822          823 
##  -7762.96933  -9348.12968   9743.03682  -1235.73590  -4384.58493  -8459.29928 
##          824          825          826          827          828          829 
##   7863.30098   7901.04923   4961.07119  -3117.65303   -726.59769   2877.37984 
##          830          831          832          833          834          835 
##   4653.31050   1608.20511  -6710.41453   2073.67076   -413.97773   2737.66418 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17249.23  20098.13  24354.68  24072.47  26427.26  23758.30  24475.05  19704.29 
##        10        11        12        13        14        15        16        17 
##  19440.69  16781.84  17559.78  14287.20  14338.40  15003.45  16701.05  15020.06 
##        18        19        20        21        22        23        24        25 
##  16055.35  15428.51  22515.23  21598.68  21078.23  22969.38  22294.96  22947.90 
##        26        27        28        29        30        31        32        33 
##  24794.97  18719.69  20446.89  28288.94  28346.63  28019.35  25647.33  27050.61 
##        34        35        36        37        38        39        40        41 
##  30895.96  31244.60  32654.62  30163.73  34139.88  37349.90  34404.23  31213.19 
##        42        43        44        45        46        47        48        49 
##  30060.27  20634.13  28157.39  30595.34  31685.20  38527.42  38021.70  42686.70 
##        50        51        52        53        54        55        56        57 
##  46925.52  39619.03  34184.21  29203.92  22344.21  28638.01  25216.59  21511.96 
##        58        59        60        61        62        63        64        65 
##  25922.97  27183.28  27480.47  27892.54  23760.98  40362.25  42207.32  37450.42 
##        66        67        68        69        70        71        72        73 
##  41659.90  46578.50  57253.09  55265.67  40499.98  38004.34  41024.06  35320.77 
##        74        75        76        77        78        79        80        81 
##  30770.28  21468.13  24671.53  20594.79  22682.11  17574.58  19591.22  18817.28 
##        82        83        84        85        86        87        88        89 
##  17844.42  15912.19  17190.73  20800.95  25221.33  26211.80  26236.77  26853.87 
##        90        91        92        93        94        95        96        97 
##  30981.43  29811.28  30811.24  28874.51  28073.85  28442.21  28849.04  22422.63 
##        98        99       100       101       102       103       104       105 
##  25439.48  18465.77  17321.22  15360.77  15660.70  16338.49  20813.81  19896.77 
##       106       107       108       109       110       111       112       113 
##  23409.98  23199.82  24876.94  27755.43  25252.05  21693.39  21968.98  24616.84 
##       114       115       116       117       118       119       120       121 
##  35487.85  33679.61  35518.19  38518.30  40475.38  38162.43  32980.34  29319.55 
##       122       123       124       125       126       127       128       129 
##  31403.62  29682.54  30864.81  38469.11  38111.36  37172.34  34033.99  35816.25 
##       130       131       132       133       134       135       136       137 
##  41217.36  40657.32  31853.77  33119.32  36310.99  32719.49  31105.80  30190.26 
##       138       139       140       141       142       143       144       145 
##  26745.24  28160.51  27930.00  25614.92  27640.55  26264.49  19862.81  22895.14 
##       146       147       148       149       150       151       152       153 
##  20720.54  23699.42  24239.73  25831.07  26013.25  27668.40  28969.91  32003.87 
##       154       155       156       157       158       159       160       161 
##  27471.35  26733.88  24287.68  30185.27  41677.87  40007.11  37370.50  42393.74 
##       162       163       164       165       166       167       168       169 
##  43803.15  47221.07  42684.32  37988.72  43417.17  59729.00  61942.43  60355.59 
##       170       171       172       173       174       175       176       177 
##  57179.41  55577.71  58281.88  57305.40  49592.97  52418.14  56162.97  56199.19 
##       178       179       180       181       182       183       184       185 
##  63332.23  53828.91  50577.94  41381.18  32915.53  36425.86  46535.29  45990.98 
##       186       187       188       189       190       191       192       193 
##  51947.26  57695.62  68484.97  73770.94  67461.91  67655.46  74729.94  70403.84 
##       194       195       196       197       198       199       200       201 
##  65924.02  55161.52  49188.14  50613.28  46205.38  38369.58  44798.11  43024.46 
##       202       203       204       205       206       207       208       209 
##  42662.21  43140.84  49938.86  58833.31  58462.69  60188.52  61845.28  65638.69 
##       210       211       212       213       214       215       216       217 
##  75110.79  67188.95  55371.05  49976.69  40967.73  37923.67  41039.16  31053.47 
##       218       219       220       221       222       223       224       225 
##  48036.97  55362.06  56264.49  79043.30  86542.71  88547.12  96142.93  87088.52 
##       226       227       228       229       230       231       232       233 
##  81083.42  80665.11  77313.11  76474.11  81213.91  82578.98  77011.18  72221.31 
##       234       235       236       237       238       239       240       241 
##  77878.27  64518.24  56550.07  48495.99  40102.64  44297.00  46451.00  39897.85 
##       242       243       244       245       246       247       248       249 
##  33573.72  43862.21  38105.73  42043.73  34303.08  33008.18  36665.61  39491.15 
##       250       251       252       253       254       255       256       257 
##  30315.25  36256.78  40060.43  45258.25  47978.16  47470.44  57777.41  75284.86 
##       258       259       260       261       262       263       264       265 
##  75099.80  68406.55  69889.25  66107.83  67554.81  61308.84  50577.19  46602.22 
##       266       267       268       269       270       271       272       273 
##  46811.75  42916.20  51725.49  47996.39  52145.55  50261.02  54330.76  54626.95 
##       274       275       276       277       278       279       280       281 
##  60647.91  58280.76  67959.39  61880.62  62090.78  60438.88  66185.12  59911.93 
##       282       283       284       285       286       287       288       289 
##  56473.77  46019.95  44415.75  61658.10  67191.44  67601.14  65017.22  64104.05 
##       290       291       292       293       294       295       296       297 
##  68107.36  72019.90  53005.27  43101.50  37109.02  47436.47  50680.21  49793.63 
##       298       299       300       301       302       303       304       305 
##  74004.20  79953.10  80621.15  85236.33  83433.84  78461.16  81930.03  56814.04 
##       306       307       308       309       310       311       312       313 
##  53072.95  52752.54  46539.04  43746.83  47352.31  39900.10  38621.44  33179.75 
##       314       315       316       317       318       319       320       321 
##  36966.54  36147.40  39981.14  37964.37  63766.02  61605.91  63230.46  71233.34 
##       322       323       324       325       326       327       328       329 
##  73622.10  99118.94  97495.64  73311.42  72108.55  70464.90  62411.82  59532.33 
##       330       331       332       333       334       335       336       337 
##  29462.76  33152.53  33592.32  35913.64  35254.08  41024.55  42100.80  37345.52 
##       338       339       340       341       342       343       344       345 
##  36559.47  36686.21  32002.36  38004.96  38668.76  38927.37  39803.70  41590.71 
##       346       347       348       349       350       351       352       353 
##  43413.80  43165.31  36105.69  26667.81  32015.72  30876.04  30465.84  28080.83 
##       354       355       356       357       358       359       360       361 
##  32762.56  36519.14  40985.47  39173.36  40434.42  42574.18  49973.94  50524.87 
##       362       363       364       365       366       367       368       369 
##  50726.66  53191.33  50673.21  50117.42  42758.11  39953.17  36127.79  33905.12 
##       370       371       372       373       374       375       376       377 
##  29960.49  37252.87  39541.23  47431.60  41399.53  40846.36  39382.77  38915.26 
##       378       379       380       381       382       383       384       385 
##  29777.31  34378.25  27425.64  35650.04  45989.48  49557.13  47829.44  49822.35 
##       386       387       388       389       390       391       392       393 
##  56046.35  65537.67  58744.77  53209.56  52938.10  60322.25  60845.95  69520.01 
##       394       395       396       397       398       399       400       401 
##  58618.91  60186.77  59746.60  59230.14  57716.10  56476.90  43227.25  51818.27 
##       402       403       404       405       406       407       408       409 
##  50818.06  49783.64  56188.95  48726.75  48037.47  46363.09  42037.62  40867.59 
##       410       411       412       413       414       415       416       417 
##  38930.24  33020.67  40898.43  43826.62  38495.57  33580.71  48461.77  52309.00 
##       418       419       420       421       422       423       424       425 
##  56240.77  48705.15  45026.12  43701.25  47290.00  35701.74  35430.77  29685.90 
##       426       427       428       429       430       431       432       433 
##  35279.31  43612.22  50508.76  47272.14  44338.96  41262.18  41150.29  37625.85 
##       434       435       436       437       438       439       440       441 
##  33761.77  30994.04  32571.14  34421.70  32425.04  37294.33  43503.83  40254.11 
##       442       443       444       445       446       447       448       449 
##  39959.91  42968.27  40852.98  44845.06  40088.90  31115.41  29952.13  41316.37 
##       450       451       452       453       454       455       456       457 
##  40997.70  46652.54  42285.86  42635.74  44257.30  47977.91  37845.62  42698.05 
##       458       459       460       461       462       463       464       465 
##  38118.34  45692.30  49214.61  51836.87  48564.29  50906.59  51108.26  52856.43 
##       466       467       468       469       470       471       472       473 
##  52353.71  55299.38  52625.92  57680.39  50935.94  48545.31  47130.54  43752.70 
##       474       475       476       477       478       479       480       481 
##  47511.27  54979.46  49402.79  51107.13  45892.59  44270.53  47105.32  36512.64 
##       482       483       484       485       486       487       488       489 
##  30070.00  31940.55  34638.98  36129.92  37096.41  30733.06  43271.45  49935.98 
##       490       491       492       493       494       495       496       497 
##  56770.71  51478.75  56314.94  63953.58  67767.47  54014.84  44586.58  42610.27 
##       498       499       500       501       502       503       504       505 
##  42933.68  43725.35  38208.49  40608.98  45913.94  51599.24  52309.38  52420.01 
##       506       507       508       509       510       511       512       513 
##  46117.85  47458.70  43715.49  46472.73  46138.20  39849.90  40981.72  40156.33 
##       514       515       516       517       518       519       520       521 
##  41262.68  43904.41  36739.90  32016.22  55921.35  64087.31  67742.62  61117.42 
##       522       523       524       525       526       527       528       529 
##  62457.77  76052.18  83033.38  57967.46  52834.20  49526.41  53908.21  53414.22 
##       530       531       532       533       534       535       536       537 
##  43591.49  48586.52  61255.15  55772.51  59172.33  63162.66  60213.36  55240.94 
##       538       539       540       541       542       543       544       545 
##  48699.03  47354.06  55296.25  55046.52  47601.80  49826.85  49653.40  50347.68 
##       546       547       548       549       550       551       552       553 
##  40990.71  32821.25  37165.83  45287.59  45084.43  46791.40  40797.41  49816.11 
##       554       555       556       557       558       559       560       561 
##  50972.15  40744.84  50291.86  58159.64  57524.18  61124.16  56888.71  68656.66 
##       562       563       564       565       566       567       568       569 
##  85355.83  75440.57  63996.91  68418.91  66542.12  67729.51  59294.20  43273.20 
##       570       571       572       573       574       575       576       577 
##  50286.24  56195.07  57378.58  59455.41  60102.85  57227.49  69481.80  58848.54 
##       578       579       580       581       582       583       584       585 
##  52567.61  60173.91  61678.83  54768.56  61039.00  56612.38  53655.10  67233.65 
##       586       587       588       589       590       591       592       593 
##  52653.14  60001.96  59093.78  52812.10  52116.83  52393.04  43103.13  45901.70 
##       594       595       596       597       598       599       600       601 
##  40524.82  44773.26  53541.96  46869.07  52731.69  55110.95  60783.52  56939.29 
##       602       603       604       605       606       607       608       609 
##  61756.00  53319.45  55200.60  55977.42  58287.00  58861.40  58401.88  52575.72 
##       610       611       612       613       614       615       616       617 
##  59641.71  57700.62  54796.03  51500.10  44458.71  55976.04  59865.85  50795.71 
##       618       619       620       621       622       623       624       625 
##  61204.08  65391.58  58877.39  81118.51  66232.20  58556.85  60561.00  55911.49 
##       626       627       628       629       630       631       632       633 
##  46240.97  56954.90  37499.36  37359.51  46933.62  57422.66  55466.08  84212.52 
##       634       635       636       637       638       639       640       641 
##  74392.79  76594.86  78233.15  72956.29  65671.16  62303.43  50201.08  48568.41 
##       642       643       644       645       646       647       648       649 
##  47459.32  45940.66  44324.35  47002.80  51622.97  66602.81  81035.72  78157.35 
##       650       651       652       653       654       655       656       657 
##  79061.66  84936.64  98389.33  93144.81  63397.16  60847.45  57800.01  58785.23 
##       658       659       660       661       662       663       664       665 
##  55224.08  45631.11  48016.87  52337.22  51528.94  63097.60  62975.23  63264.92 
##       666       667       668       669       670       671       672       673 
##  51713.50  53073.83  54092.89  49428.89  43405.06  46441.76  44039.15  47528.00 
##       674       675       676       677       678       679       680       681 
##  45279.10  38112.47  32767.31  32764.81  35515.44  40250.23  42512.00  40515.33 
##       682       683       684       685       686       687       688       689 
##  40538.93  40987.96  35327.01  41660.14  41157.29  41456.72  43453.39  54167.43 
##       690       691       692       693       694       695       696       697 
##  62629.71  70683.67  59974.24  55979.67  52860.18  58017.28  48304.44  41999.78 
##       698       699       700       701       702       703       704       705 
##  35891.92  32609.10  31088.19  36598.05  34860.62  35533.67  41470.08  70146.35 
##       706       707       708       709       710       711       712       713 
##  76308.54  93865.93  90183.03  92779.82 107813.72 106653.69  84002.37  84349.88 
##       714       715       716       717       718       719       720       721 
##  75683.19  72784.22  70759.59  53479.28  48874.97  52369.07  49872.43  38981.31 
##       722       723       724       725       726       727       728       729 
##  44552.49  40821.64  43068.04  40942.01  31643.98  35596.97  36222.57  29854.48 
##       730       731       732       733       734       735       736       737 
##  47932.45  50181.35  48214.16  53871.24  46273.56  46548.28  54534.18  40978.10 
##       738       739       740       741       742       743       744       745 
##  37388.10  45894.08  42666.83  44170.26  46940.49  46053.17  42470.29  49462.98 
##       746       747       748       749       750       751       752       753 
##  44481.31  65455.13  70778.32  66885.51  58816.45  78606.26  71676.75  70595.38 
##       754       755       756       757       758       759       760       761 
##  55824.58 104571.11 121650.88 126242.33 107758.28 110187.87 109434.90 107463.09 
##       762       763       764       765       766       767       768       769 
##  60115.47  45158.48  47068.73  45692.55  43668.04 152297.59 156736.83 181954.59 
##       770       771       772       773       774       775       776       777 
## 185522.98 179457.45 177453.79 184339.59  81502.86  72087.08  38444.00  41878.78 
##       778       779       780       781       782       783       784       785 
##  42287.98  46688.00  41083.86  41404.03  41246.57  45802.56  40537.06  40234.38 
##       786       787       788       789       790       791       792       793 
##  45351.03  48377.36  46630.94  43978.84  46718.97  50088.32  50494.40  45035.23 
##       794       795       796       797       798       799       800       801 
##  41069.13  41654.39  42064.71  36694.45  36776.24  36048.50  35939.12  47547.36 
##       802       803       804       805       806       807       808       809 
##  50278.00  56893.96  59043.59  53605.02  60770.91  68508.82  57373.46  50443.95 
##       810       811       812       813       814       815       816       817 
##  44293.01  48105.02  52475.45  50645.24  38648.28  36951.93  44533.76  52888.52 
##       818       819       820       821       822       823       824       825 
##  44535.26  38916.13  32618.96  43802.02  43980.58  41384.30  35553.27  44723.81 
##       826       827       828       829       830       831       832       833 
##  52772.64  57238.22  54080.03  53409.48  55973.55  59767.08  60421.27  53721.90 
##       834       835 
##  55544.12  54962.48 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8105
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    5.131378   0.7657782    3.877284
## t2* 2686.032476 164.6887957  889.637081
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value  Upper CI
## 1 interrupt_var    1.148163       5.109093   13.3986
## 2    lag_depvar 1641.308991    2732.390646 4518.6302

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
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#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 6.520667 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 316.617667 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 57.448667 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 4.396667 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.000000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 27.483333 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 30.833000 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 14.663333 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.000000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.000000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 457.963333 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2685, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
    

fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2685 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-05-09 00:04:58 sería de: 26.714 pesos// Percentil 95% más alto proyectado: 35.134,38

Según TimeGPT: La proyección de la UF a 298 días más 2026-03-03 sería de: 40.071,14 pesos// Percentil 80% más alto proyectado: 40.455,09 pesos// Percentil 95% más alto proyectado: 41.529,13

Según prophet: La proyección de la UF a 298 días más 2026-03-03 sería de: 40.248 pesos// Percentil 95% más alto proyectado: 44.723

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26328.94 26324.01
Lo.80 26461.63 26488.42
Point.Forecast 26714.13 26799.01
Hi.80 31516.13 32160.60
Hi.95 34398.32 34998.86


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,1,1) 
## 
## Coefficients:
##          ar1      ma1
##       0.3483  -0.9346
## s.e.  0.1374   0.0704
## 
## sigma^2 = 38816:  log likelihood = -488.95
## AIC=983.91   AICc=984.26   BIC=990.78
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.3520   564.4043  14.9274
## s.e.  0.1125   301.6200   9.2576
## 
## sigma^2 = 37056:  log likelihood = -492.78
## AIC=993.57   AICc=994.15   BIC=1002.78
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 674.8956 656.9731 697.1199
Lo.80 814.4207 806.2363 793.4172
Point.Forecast 1077.9901 1089.9752 1012.6503
Hi.80 1341.5594 1379.9564 1291.7041
Hi.95 1481.0846 1533.4632 1468.9823


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.6         Rcpp_1.0.14        
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.13     
## [10] tidytext_0.4.2      DT_0.33             janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [16] xts_0.14.1          forecast_8.24.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-16          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.4        
## [28] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.8.17       lattice_0.22-6     
## [34] GGally_2.2.1        ggplot2_3.5.2       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.9.0      httr_1.4.7         
## [40] readxl_1.4.5        zoo_1.8-14          stringr_1.5.1      
## [43] stringi_1.8.7       DataExplorer_0.8.3  data.table_1.17.0  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9        cellranger_1.1.0    datawizard_1.0.2   
##   [4] httr2_1.1.2         lifecycle_1.0.4     StanHeaders_2.32.10
##   [7] doParallel_1.0.17   globals_0.16.3      vroom_1.6.5        
##  [10] MASS_7.3-60.2       insight_1.1.0       crosstalk_1.2.1    
##  [13] magrittr_2.0.3      sass_0.4.10         rmarkdown_2.29     
##  [16] jquerylib_0.1.4     yaml_2.3.10         fracdiff_1.5-3     
##  [19] doRNG_1.8.6.2       askpass_1.2.1       pkgbuild_1.4.7     
##  [22] DBI_1.2.3           abind_1.4-8         quadprog_1.5-8     
##  [25] nnet_7.3-19         rappdirs_0.3.3      sandwich_3.1-1     
##  [28] inline_0.3.21       tokenizers_0.3.0    listenv_0.9.1      
##  [31] anytime_0.3.11      performance_0.13.0  spatial_7.3-17     
##  [34] parallelly_1.43.0   codetools_0.2-20    xml2_1.3.8         
##  [37] tidyselect_1.2.1    ggeffects_2.2.1     farver_2.1.2       
##  [40] urca_1.3-4          its.analysis_1.6.0  matrixStats_1.5.0  
##  [43] stats4_4.4.0        jsonlite_2.0.0      ellipsis_0.3.2     
##  [46] Formula_1.2-5       iterators_1.0.14    systemfonts_1.2.2  
##  [49] foreach_1.5.2       tools_4.4.0         glue_1.8.0         
##  [52] xfun_0.52           TTR_0.24.4          ggfortify_0.4.17   
##  [55] loo_2.8.0           withr_3.0.2         timeSeries_4041.111
##  [58] fastmap_1.2.0       boot_1.3-30         openssl_2.3.2      
##  [61] caTools_1.18.3      digest_0.6.37       timechange_0.3.0   
##  [64] R6_2.6.1            lfe_3.1.1           colorspace_2.1-1   
##  [67] networkD3_0.4       gtools_3.9.5        generics_0.1.3     
##  [70] htmlwidgets_1.6.4   ggstats_0.9.0       pkgconfig_2.0.3    
##  [73] gtable_0.3.6        timeDate_4041.110   lmtest_0.9-40      
##  [76] selectr_0.4-2       janeaustenr_1.0.0   htmltools_0.5.8.1  
##  [79] carData_3.0-5       tseries_0.10-58     snakecase_0.11.1   
##  [82] knitr_1.50          rstudioapi_0.17.1   tzdb_0.5.0         
##  [85] uuid_1.2-1          nlme_3.1-164        curl_6.2.2         
##  [88] cachem_1.1.0        sjlabelled_1.2.0    KernSmooth_2.23-22 
##  [91] parallel_4.4.0      fBasics_4041.97     pillar_1.10.2      
##  [94] vctrs_0.6.5         gplots_3.2.0        slam_0.1-55        
##  [97] car_3.1-3           dbplyr_2.5.0        xtable_1.8-4       
## [100] evaluate_1.0.3      mvtnorm_1.3-3       cli_3.6.4          
## [103] compiler_4.4.0      crayon_1.5.3        rngtools_1.5.2     
## [106] future.apply_1.11.3 labeling_0.4.3      sjmisc_2.8.10      
## [109] rstan_2.32.7        QuickJSR_1.7.0      viridisLite_0.4.2  
## [112] assertthat_0.2.1    munsell_0.5.1       lazyeval_0.2.2     
## [115] Matrix_1.7-0        sjstats_0.19.0      hms_1.1.3          
## [118] bit64_4.6.0-1       future_1.40.0       nixtlar_0.6.2      
## [121] extraDistr_1.10.0   igraph_2.1.4        RcppParallel_5.1.10
## [124] bslib_0.9.0         quantmod_0.4.27     bit_4.6.0
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))